Sarcoidosis scRNA-seq: Quality control and clustering BAL Samples

Contents:¶

  • Sarcoidosis scRNA-seq:Quality control and clustering BAL Samples
  • Importing all necessary python packages
  • Meta data of Samples
  • Loading Data
  • Preprocessing
  • Basic Filter
  • Pre QC metrics calculation: Sample wise
  • Post QC metrics cutoffs: Sample wise
  • Normalization: Counts Per Million (CPM)
  • Logarithmize
  • Highly-variable genes filtering
  • Regressing out: total_counts', 'pct_counts_mt','pct_counts_ribo' to all samples
  • Scaling
  • PCA Analysis
  • Saving data
  • Reading dataset
  • Computing the neighborhood graph and clustering
  • Saving data after neighborhood graph and clustering

Sarcoidosis scRNA-seq: Quality control and clustering BAL Samples ¶

  • Three Sarcoidosis Bronchoalveolar lavage (BAL) datasets were sequenced by Illumina NovaSeq 6000 and processed by Pipeline-Version: cellranger-7.1.0

  • Ten Healthy Samples Bronchoalveolar lavage (BAL) datasets were sequenced by Illumina NovaSeq 6000 and and processed by Pipeline-Version: oldercellranger. All Healthy Samples aged 18–50 years https://cells.ucsc.edu/?ds=healthy-bal

Importing all necessary python packages ¶

In [1]:
# Import necessary libraries

import scanpy as sc #software suite of tools for single-cell analysis in python
import besca as bc #internal BEDA package for single cell analysis
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import scipy
import anndata as ad
from scipy.sparse import csr_matrix
import scanpy.external as sce
from harmony import harmonize
import umap.umap_ as umap
import os
from scipy import io
print(ad.__version__)

sc.settings.verbosity = 3             # verbosity: errors (0), warnings (1), info (2), hints (3)

INFO:torch.distributed.nn.jit.instantiator:Created a temporary directory at /tmp/tmpgeu6z05w
INFO:torch.distributed.nn.jit.instantiator:Writing /tmp/tmpgeu6z05w/_remote_module_non_scriptable.py
INFO:lightning_fabric.utilities.seed:Global seed set to 0

0.9.1

Meta data of Samples ¶

Loading all BAL datasets into the workspace

  1. balsarc1: SAM24412249-Sarcoidosis_Donor1_BAL-male-57yrs-white from Genentech (10x Genomics Chromium v3.1 3’ NovaSeq 6000)

  2. balsarc2: SAM24412250 Sarcoidosis_Donor2_PBMC: male-35yrs-southasisan sequenced by Genentech (10x Genomics Chromium v3.1 3’ NovaSeq 6000)

  3. balsarc3: SAM24412251 Sarcoidosis_Donor3_PBMC: female-60yrs-white sequenced by Genentech (10x Genomics Chromium v3.1 3’ NovaSeq 6000)

10 Healthy BAL samples collected from NCBI GEO link

Cite: Mould KJ, Moore CM, McManus SA, McCubbrey AL, McClendon JD, Griesmer CL, Henson PM, Janssen WJ. Airspace Macrophages and Monocytes Exist in Transcriptionally Distinct Subsets in Healthy Adults. Am J Respir Crit Care Med. 2021 Apr 15;203(8):946-956. doi: 10.1164/rccm.202005-1989OC. PMID: 33079572; PMCID: PMC8048748. Format:

  1. balhealthy1: GSM4593888. For more info Link

  2. balhealthy2: GSM4593889 For more info Link

  3. balhealthy3: GSM4593890. For more info Link

  4. balhealthy4: GSM4593891. For more info Link

  5. balhealthy5: GSM4593892. For more info Link

  6. balhealthy6: GSM4593893. For more info Link

  7. balhealthy7: GSM4593894. For more info Link

  8. balhealthy8: GSM4593895. For more info Link

  9. balhealthy9: GSM4593896. For more info Link

  10. balhealthy10: GSM4593897. For more info Link

Loading Data ¶

In [2]:
#loading all BAL datasets into the workspace

#Load Disease BAL dataset1 for sarcoidosis  
balsarc1=sc.read_10x_mtx('/raid02/Data-live/tjana/LIB5455298_SAM24412249/outs/filtered_feature_bc_matrix/', 
                      var_names='gene_symbols',cache=True)

#Load Disease BAL dataset2 for sarcoidosis  
balsarc2=sc.read_10x_mtx('/raid02/Data-live/tjana/LIB5455300_SAM24412251/outs/filtered_feature_bc_matrix/', 
                      var_names='gene_symbols',cache=True)

#Load Disease PBMC dataset1 for sarcoidosis  
balsarc3=sc.read_10x_mtx('/raid02/Data-live/tjana/LIB5455302_SAM24412253/outs/filtered_feature_bc_matrix/', 
                      var_names='gene_symbols',cache=True)

#load Healthy BAL Control1
balhealthy1=sc.read_10x_mtx('/raid02/Data-live/tjana/healthy_BAL/GSM4593888/', 
                         var_names='gene_symbols', cache=True)

#load Healthy BAL Control2
balhealthy2=sc.read_10x_mtx('/raid02/Data-live/tjana/healthy_BAL/GSM4593889/', 
                         var_names='gene_symbols',cache=True)

#load Healthy BAL Control3
balhealthy3=sc.read_10x_mtx('/raid02/Data-live/tjana/healthy_BAL/GSM4593890/', 
                         var_names='gene_symbols',cache=True)

#load Healthy BAL Control4
balhealthy4=sc.read_10x_mtx('/raid02/Data-live/tjana/healthy_BAL/GSM4593891/', 
                         var_names='gene_symbols',cache=True)
#load Healthy BAL Control5
balhealthy5=sc.read_10x_mtx('/raid02/Data-live/tjana/healthy_BAL/GSM4593892/', 
                         var_names='gene_symbols',cache=True)


#load Healthy BAL Control6
balhealthy6=sc.read_10x_mtx('/raid02/Data-live/tjana/healthy_BAL/GSM4593893/', 
                         var_names='gene_symbols',cache=True)

#load Healthy BAL Control7
balhealthy7=sc.read_10x_mtx('/raid02/Data-live/tjana/healthy_BAL/GSM4593894/', 
                         var_names='gene_symbols',cache=True)
                         

#load Healthy BAL Control8
balhealthy8=sc.read_10x_mtx('/raid02/Data-live/tjana/healthy_BAL/GSM4593895/', 
                         var_names='gene_symbols',cache=True)
#load Healthy BAL Control9
balhealthy9=sc.read_10x_mtx('/raid02/Data-live/tjana/healthy_BAL/GSM4593896/', 
                         var_names='gene_symbols',cache=True)

#load Healthy BAL Control10
balhealthy10=sc.read_10x_mtx('/raid02/Data-live/tjana/healthy_BAL/GSM4593897/', 
                         var_names='gene_symbols',cache=True)

... reading from cache file cache/raid02-Data-live-tjana-LIB5455298_SAM24412249-outs-filtered_feature_bc_matrix-matrix.h5ad
... reading from cache file cache/raid02-Data-live-tjana-LIB5455300_SAM24412251-outs-filtered_feature_bc_matrix-matrix.h5ad
... reading from cache file cache/raid02-Data-live-tjana-LIB5455302_SAM24412253-outs-filtered_feature_bc_matrix-matrix.h5ad
... reading from cache file cache/raid02-Data-live-tjana-healthy_BAL-GSM4593888-matrix.h5ad
... reading from cache file cache/raid02-Data-live-tjana-healthy_BAL-GSM4593889-matrix.h5ad
... reading from cache file cache/raid02-Data-live-tjana-healthy_BAL-GSM4593890-matrix.h5ad
... reading from cache file cache/raid02-Data-live-tjana-healthy_BAL-GSM4593891-matrix.h5ad
... reading from cache file cache/raid02-Data-live-tjana-healthy_BAL-GSM4593892-matrix.h5ad
... reading from cache file cache/raid02-Data-live-tjana-healthy_BAL-GSM4593893-matrix.h5ad
... reading from cache file cache/raid02-Data-live-tjana-healthy_BAL-GSM4593894-matrix.h5ad
... reading from cache file cache/raid02-Data-live-tjana-healthy_BAL-GSM4593895-matrix.h5ad
... reading from cache file cache/raid02-Data-live-tjana-healthy_BAL-GSM4593896-matrix.h5ad
... reading from cache file cache/raid02-Data-live-tjana-healthy_BAL-GSM4593897-matrix.h5ad

Make variable names unique for each dataset

In [3]:
# Make variable names unique for each dataset

#Making all indexes into unique of all samples (Disease SARCOIDOSIS and Healthy)

#Sarcoidosis disease

balsarc1.var_names_make_unique()
balsarc2.var_names_make_unique()
balsarc3.var_names_make_unique()

#Healthy/control

balhealthy1.var_names_make_unique()
balhealthy2.var_names_make_unique()
balhealthy3.var_names_make_unique()
balhealthy4.var_names_make_unique()
balhealthy5.var_names_make_unique()
balhealthy6.var_names_make_unique()
balhealthy7.var_names_make_unique()
balhealthy8.var_names_make_unique()
balhealthy9.var_names_make_unique()
balhealthy10.var_names_make_unique()

Adding some metadata for all PBMC samples

In [4]:
# Adding some metadata for all BAL samples

balsarc1.obs['type']="Sarcoidosis"
balsarc1.obs['sample']="BAL-Sarc-1"
balsarc2.obs['type']="Sarcoidosis"
balsarc2.obs['sample']="BAL-Sarc-2"
balsarc3.obs['type']="Sarcoidosis"
balsarc3.obs['sample']="BAL-Sarc-3"


balhealthy1.obs['type']="Healthy"
balhealthy1.obs['sample']="BAL-healthy-1"
balhealthy2.obs['type']="Healthy"
balhealthy2.obs['sample']="BAL-healthy-2"
balhealthy3.obs['type']="Healthy"
balhealthy3.obs['sample']="BAL-healthy-3"
balhealthy4.obs['type']="Healthy"
balhealthy4.obs['sample']="BAL-healthy-4"
balhealthy5.obs['type']="Healthy"
balhealthy5.obs['sample']="BAL-healthy-5"
balhealthy6.obs['type']="Healthy"
balhealthy6.obs['sample']="BAL-healthy-6"
balhealthy7.obs['type']="Healthy"
balhealthy7.obs['sample']="BAL-healthy-7"
balhealthy8.obs['type']="Healthy"
balhealthy8.obs['sample']="BAL-healthy-8"
balhealthy9.obs['type']="Healthy"
balhealthy9.obs['sample']="BAL-healthy-9"
balhealthy10.obs['type']="Healthy"
balhealthy10.obs['sample']="BAL-healthy-10"

Preprocessing ¶

Explore the loaded data before preprocessing for each dataset using a for loop

In [5]:
# Explore the loaded data for each dataset using a for loop

for adata_list in [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]:
    display(adata_list)

AnnData object with n_obs × n_vars = 11727 × 36601
    obs: 'type', 'sample'
    var: 'gene_ids', 'feature_types'
AnnData object with n_obs × n_vars = 13340 × 36601
    obs: 'type', 'sample'
    var: 'gene_ids', 'feature_types'
AnnData object with n_obs × n_vars = 5883 × 36601
    obs: 'type', 'sample'
    var: 'gene_ids', 'feature_types'
AnnData object with n_obs × n_vars = 6816 × 33538
    obs: 'type', 'sample'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 6719 × 33538
    obs: 'type', 'sample'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5007 × 33538
    obs: 'type', 'sample'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5265 × 33538
    obs: 'type', 'sample'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5826 × 33538
    obs: 'type', 'sample'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5423 × 33538
    obs: 'type', 'sample'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5773 × 33538
    obs: 'type', 'sample'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 6690 × 33538
    obs: 'type', 'sample'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 2971 × 33538
    obs: 'type', 'sample'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 4356 × 33538
    obs: 'type', 'sample'
    var: 'gene_ids'

chromosomeY: male and XIST expression: female computing

In [6]:
# chromosomeY: males and XIST (X-inactive specific transcript):female

def get_biomart_annotations(species, gene_info):
    return sc.queries.biomart_annotations(species, gene_info).set_index("external_gene_name")

def chromosomeY_adjustment_step1(adata, species="hsapiens", gene_info=["ensembl_gene_id", "external_gene_name", "start_position", "end_position", "chromosome_name"]):
    annot = get_biomart_annotations(species, gene_info)
    chrY_genes = adata.var_names.intersection(annot.index[annot.chromosome_name == "Y"])
    return chrY_genes

def calculate_percent_chrY(adata, chrY_genes):
    adata.obs['percent_chrY'] = np.sum(adata[:, chrY_genes].X, axis=1).A1 / np.sum(adata.X, axis=1).A1 * 100

def add_XIST_expression_to_obs(adata):
    adata.obs["XIST-counts"] = adata.X[:, adata.var_names.str.match('XIST')].toarray()


# Example usage

i=0
for adata in [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]:
    chrY_genes = chromosomeY_adjustment_step1(adata)
    calculate_percent_chrY(adata, chrY_genes)
    add_XIST_expression_to_obs(adata)
    i=i+1

Explore the dataset after chromosomeY: male and XIST expression: female computing

In [7]:
for adata_list in [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]:
    display(adata_list)

AnnData object with n_obs × n_vars = 11727 × 36601
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids', 'feature_types'
AnnData object with n_obs × n_vars = 13340 × 36601
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids', 'feature_types'
AnnData object with n_obs × n_vars = 5883 × 36601
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids', 'feature_types'
AnnData object with n_obs × n_vars = 6816 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 6719 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5007 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5265 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5826 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5423 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5773 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 6690 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 2971 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 4356 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
In [8]:
# Adding some metadata for all BAL samples

adata = balsarc1.concatenate(balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10)
sc.pl.violin(adata, ["XIST-counts", "percent_chrY"], jitter=0.4, groupby = 'sample', rotation= 90)
del(adata)

/home/jana/my-notebook-venv/lib/python3.8/site-packages/anndata/_core/anndata.py:1755: FutureWarning:

The AnnData.concatenate method is deprecated in favour of the anndata.concat function. Please use anndata.concat instead.

See the tutorial for concat at: https://anndata.readthedocs.io/en/latest/concatenation.html

... storing 'type' as categorical
... storing 'sample' as categorical
... storing 'feature_types-0' as categorical
... storing 'feature_types-1' as categorical
... storing 'feature_types-2' as categorical
In [9]:
for adata_list in [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]:
    display(adata_list)

AnnData object with n_obs × n_vars = 11727 × 36601
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids', 'feature_types'
AnnData object with n_obs × n_vars = 13340 × 36601
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids', 'feature_types'
AnnData object with n_obs × n_vars = 5883 × 36601
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids', 'feature_types'
AnnData object with n_obs × n_vars = 6816 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 6719 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5007 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5265 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5826 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5423 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 5773 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 6690 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 2971 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'
AnnData object with n_obs × n_vars = 4356 × 33538
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts'
    var: 'gene_ids'

Visualize highest expression genes for each dataset

In [10]:
# Visualize highest expression genes for each dataset in separate panels using a for loop
# Explore the loaded data for each dataset using a for loop


adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]

def highest_expr_genes(adata_list, n_top=20):
    for i, adata in enumerate(adata_list):
        sc.pl.highest_expr_genes(adata, n_top=n_top, show=False)
        plt.title(f'sample {i+1}')
        plt.show()
        
        # Tabular form
        df = pd.DataFrame(adata[:, adata.var_names].X.sum(axis=0).A1, index=adata.var_names, columns=['Total Expression'])
        df = df.sort_values(by='Total Expression', ascending=False)[:n_top]
        print(f"Top {n_top} expressed genes in Dataset {i+1}:")
        print(df)

# Example usage:
highest_expr_genes(adata_list, n_top=20)

normalizing counts per cell
    finished (0:00:00)

Top 20 expressed genes in Dataset 1:
          Total Expression
FTL              4062825.0
FTH1             2497482.0
B2M              1097072.0
MALAT1           1056347.0
HLA-DRA          1021517.0
MT-CO1            999195.0
MT-CO3            989242.0
TMSB4X            968801.0
MT-CO2            882213.0
CD74              730477.0
HLA-DRB1          727767.0
MT-ND3            633884.0
LYZ               564110.0
MT-ATP6           546354.0
MT-CYB            501582.0
APOC1             498152.0
TPT1              474481.0
EEF1A1            463938.0
VIM               453250.0
MT-ND4            451690.0
normalizing counts per cell
    finished (0:00:00)

Top 20 expressed genes in Dataset 2:
          Total Expression
FTL              3141337.0
FTH1             1027058.0
MALAT1            825806.0
MT-CO1            682509.0
B2M               679199.0
MT-CO3            648213.0
TMSB4X            639935.0
HLA-DRA           544669.0
MT-CO2            498588.0
LYZ               482202.0
MT-ATP6           461357.0
APOC1             458132.0
HLA-DRB1          413790.0
CD74              403105.0
MT-ND3            386432.0
MT-CYB            297518.0
TPT1              271147.0
MT-ND4            256590.0
TMSB10            244787.0
EEF1A1            243159.0
normalizing counts per cell
    finished (0:00:00)

Top 20 expressed genes in Dataset 3:
          Total Expression
FTL              4102206.0
FTH1             1976169.0
MT-CO1            968837.0
MT-CO3            962896.0
MALAT1            954740.0
TMSB4X            940683.0
MT-CO2            894676.0
B2M               877783.0
MT-ATP6           656765.0
MT-ND3            562322.0
HLA-DRA           540661.0
MT-CYB            515631.0
MT-ND4            421075.0
CD74              382996.0
HLA-DRB1          373436.0
MT-ND1            371728.0
MT-ND2            363022.0
VIM               361412.0
S100A6            345059.0
APOC1             343322.0
normalizing counts per cell
    finished (0:00:00)

Top 20 expressed genes in Dataset 4:
         Total Expression
FTL            10395906.0
FTH1            5451005.0
MT-CO3          2731158.0
MALAT1          2543177.0
MT-CO1          2472719.0
TMSB4X          1905243.0
MT-CO2          1852347.0
MT-ND3          1831130.0
MT-ATP6         1775498.0
B2M             1662683.0
HLA-DRA         1471854.0
MT-CYB          1387605.0
MT-ND4          1181140.0
APOC1           1104697.0
MT-ND1          1033697.0
TPT1            1012083.0
LYZ              939680.0
EEF1A1           905331.0
CD74             863275.0
MT-ND2           841362.0
normalizing counts per cell
    finished (0:00:00)

Top 20 expressed genes in Dataset 5:
         Total Expression
FTL            10598689.0
FTH1            4131785.0
MT-CO3          2599068.0
MALAT1          2559522.0
MT-CO1          2450207.0
MT-CO2          1987583.0
TMSB4X          1935362.0
MT-ATP6         1651073.0
MT-CYB          1599830.0
MT-ND3          1522507.0
B2M             1515426.0
MT-ND4          1325162.0
APOC1           1206615.0
LYZ             1156868.0
HLA-DRA         1148836.0
MT-ND1          1101885.0
MT-ND2           927675.0
S100A6           861823.0
VIM              823698.0
TPT1             817241.0
normalizing counts per cell
    finished (0:00:00)

Top 20 expressed genes in Dataset 6:
         Total Expression
FTL             7165733.0
FTH1            3613302.0
MT-CO3          2817990.0
MT-CO1          2572089.0
MALAT1          2088777.0
MT-CO2          1952390.0
TMSB4X          1821704.0
MT-ATP6         1729661.0
MT-CYB          1723181.0
B2M             1577005.0
MT-ND3          1566510.0
HLA-DRA         1445919.0
MT-ND4          1212223.0
MT-ND1           998701.0
MT-ND2           919433.0
CD74             867264.0
VIM              859888.0
APOC1            819989.0
S100A6           776640.0
TPT1             727816.0
normalizing counts per cell
    finished (0:00:00)

Top 20 expressed genes in Dataset 7:
          Total Expression
FTL              6717957.0
FTH1             3055321.0
MALAT1           2128891.0
TMSB4X           1402581.0
MT-CO1           1220682.0
B2M              1155019.0
HLA-DRA          1130530.0
MT-CO3           1118556.0
LYZ               948417.0
APOC1             917127.0
MT-ND3            891614.0
HLA-DRB1          841454.0
MT-CO2            792905.0
TPT1              784884.0
MT-ATP6           670676.0
MT-ND1            657484.0
EEF1A1            636714.0
CD74              636650.0
MT-CYB            627246.0
MT-ND4            565462.0
normalizing counts per cell
    finished (0:00:00)

Top 20 expressed genes in Dataset 8:
         Total Expression
FTL             9209223.0
FTH1            3988021.0
MALAT1          1964667.0
LYZ             1860415.0
TMSB4X          1859977.0
MT-CO3          1851935.0
MT-CO2          1773114.0
MT-CO1          1620421.0
MT-CYB          1567909.0
B2M             1374146.0
APOC1           1326064.0
HLA-DRA         1255156.0
MT-ND4          1240598.0
MT-ND2          1161049.0
MT-ATP6         1160996.0
MT-ND3          1144439.0
TPT1            1003851.0
MT-ND1           958549.0
EEF1A1           917186.0
VIM              841938.0
normalizing counts per cell
    finished (0:00:00)

Top 20 expressed genes in Dataset 9:
          Total Expression
FTL              6821410.0
FTH1             3314335.0
MT-CO3           3017850.0
MT-CO1           2319422.0
MALAT1           2233772.0
MT-CO2           1983552.0
TMSB4X           1719950.0
MT-ATP6          1705501.0
MT-ND3           1681387.0
HLA-DRA          1503728.0
MT-CYB           1491584.0
MT-ND4           1464066.0
B2M              1389004.0
APOC1            1366991.0
HLA-DRB1         1296554.0
MT-ND1           1289340.0
MT-ND2           1080162.0
LYZ              1008273.0
CD74              936236.0
TPT1              848005.0
normalizing counts per cell
    finished (0:00:00)

Top 20 expressed genes in Dataset 10:
         Total Expression
FTL             4726097.0
MT-CO3          3273444.0
MT-CO1          2929278.0
FTH1            2743566.0
MT-CO2          2286702.0
MALAT1          2164077.0
MT-CYB          1988158.0
MT-ATP6         1862629.0
MT-ND3          1811964.0
MT-ND4          1679469.0
TMSB4X          1597366.0
MT-ND2          1366825.0
MT-ND1          1317631.0
B2M             1095978.0
HLA-DRA         1093788.0
CD74             789387.0
S100A6           681196.0
TPT1             671329.0
LYZ              646042.0
VIM              633655.0
normalizing counts per cell
    finished (0:00:00)

Top 20 expressed genes in Dataset 11:
          Total Expression
FTL              9409857.0
FTH1             4087084.0
MALAT1           2195627.0
HLA-DRA          2096456.0
MT-CO3           2059806.0
TMSB4X           2054778.0
B2M              1948718.0
MT-CO2           1606590.0
MT-CO1           1576338.0
MT-ND3           1388747.0
MT-CYB           1368434.0
MT-ATP6          1313638.0
APOC1            1264124.0
MT-ND4           1158546.0
CD74             1072526.0
MT-ND2           1041186.0
TPT1              999011.0
MT-ND1            962553.0
HLA-DRB1          879566.0
S100A11           860008.0
normalizing counts per cell
    finished (0:00:00)

Top 20 expressed genes in Dataset 12:
          Total Expression
FTL              2539220.0
FTH1             1872641.0
TMSB4X           1058601.0
MT-CO1            641349.0
B2M               408778.0
MALAT1            407957.0
MT-CO3            369802.0
MT-CO2            366424.0
CD74              360652.0
MT-ND2            282371.0
APOC1             280711.0
EEF1A1            249987.0
TMSB10            242339.0
S100A6            236386.0
HLA-DRA           226841.0
RPL41             178863.0
MT-CYB            172857.0
VIM               170940.0
HLA-DRB1          170695.0
MT-ND4            170018.0
normalizing counts per cell
    finished (0:00:00)

Top 20 expressed genes in Dataset 13:
         Total Expression
FTL             4566062.0
FTH1            2977017.0
MT-CO3          2168220.0
MT-CO1          1894855.0
MT-ATP6         1596445.0
TMSB4X          1380306.0
MT-CO2          1373828.0
MALAT1          1254660.0
MT-CYB          1221261.0
MT-ND3          1057914.0
B2M              998849.0
MT-ND4           931870.0
MT-ND1           896048.0
MT-ND2           756042.0
HLA-DRA          754216.0
VIM              569197.0
APOC1            510769.0
S100A6           503668.0
TPT1             477963.0
S100A11          458586.0

Basic Filter ¶

In [11]:
print ("filtering out genes in less than 3 cells")

for adata_list in [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]:
    sc.pp.filter_cells(adata_list, min_genes=200)
    sc.pp.filter_genes(adata_list, min_cells=3)

filtering out genes in less than 3 cells
filtered out 36 cells that have less than 200 genes expressed
filtered out 16670 genes that are detected in less than 3 cells
filtered out 381 cells that have less than 200 genes expressed
filtered out 16337 genes that are detected in less than 3 cells
filtered out 664 cells that have less than 200 genes expressed
filtered out 16117 genes that are detected in less than 3 cells
filtered out 291 cells that have less than 200 genes expressed
filtered out 14072 genes that are detected in less than 3 cells
filtered out 205 cells that have less than 200 genes expressed
filtered out 14013 genes that are detected in less than 3 cells
filtered out 167 cells that have less than 200 genes expressed
filtered out 14315 genes that are detected in less than 3 cells
filtered out 108 cells that have less than 200 genes expressed
filtered out 15210 genes that are detected in less than 3 cells
filtered out 107 cells that have less than 200 genes expressed
filtered out 14516 genes that are detected in less than 3 cells
filtered out 125 cells that have less than 200 genes expressed
filtered out 14425 genes that are detected in less than 3 cells
filtered out 172 cells that have less than 200 genes expressed
filtered out 14632 genes that are detected in less than 3 cells
filtered out 66 cells that have less than 200 genes expressed
filtered out 14428 genes that are detected in less than 3 cells
filtered out 279 cells that have less than 200 genes expressed
filtered out 18574 genes that are detected in less than 3 cells
filtered out 237 cells that have less than 200 genes expressed
filtered out 16014 genes that are detected in less than 3 cells

Pre QC metrics calculation: Sample wise ¶

In [12]:
# Identifying mitochondrial genes and ribosomal genes and then calculate QC metrics for each dataset
i=1
for adata_list in [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]:
    adata_list.var['mt'] = adata_list.var_names.str.startswith('MT-') # mitochondrial genes 'MT-''
    adata_list.var['ribo'] = adata_list.var_names.str.startswith(("RPS","RPL")) # ribosomal genes 'RPS/RPL'
    sc.pp.calculate_qc_metrics(adata_list, qc_vars=['mt', 'ribo'], percent_top=None, log1p=False, inplace=True)
    display ("sample no"+str(i))
    sc.pl.violin(adata_list, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt', 'pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)
    i=i+1

'sample no1'
... storing 'type' as categorical
... storing 'sample' as categorical
... storing 'feature_types' as categorical

'sample no2'
... storing 'type' as categorical
... storing 'sample' as categorical
... storing 'feature_types' as categorical

'sample no3'
... storing 'type' as categorical
... storing 'sample' as categorical
... storing 'feature_types' as categorical

'sample no4'
... storing 'type' as categorical
... storing 'sample' as categorical

'sample no5'
... storing 'type' as categorical
... storing 'sample' as categorical

'sample no6'
... storing 'type' as categorical
... storing 'sample' as categorical

'sample no7'
... storing 'type' as categorical
... storing 'sample' as categorical

'sample no8'
... storing 'type' as categorical
... storing 'sample' as categorical

'sample no9'
... storing 'type' as categorical
... storing 'sample' as categorical

'sample no10'
... storing 'type' as categorical
... storing 'sample' as categorical

'sample no11'
... storing 'type' as categorical
... storing 'sample' as categorical

'sample no12'
... storing 'type' as categorical
... storing 'sample' as categorical

'sample no13'
... storing 'type' as categorical
... storing 'sample' as categorical

Post QC metrics cutoffs: Sample wise ¶

In [13]:
for adata_list in [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]:
    display(adata_list)

AnnData object with n_obs × n_vars = 11691 × 19931
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'feature_types', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
AnnData object with n_obs × n_vars = 12959 × 20264
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'feature_types', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
AnnData object with n_obs × n_vars = 5219 × 20484
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'feature_types', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
AnnData object with n_obs × n_vars = 6525 × 19466
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
AnnData object with n_obs × n_vars = 6514 × 19525
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
AnnData object with n_obs × n_vars = 4840 × 19223
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
AnnData object with n_obs × n_vars = 5157 × 18328
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
AnnData object with n_obs × n_vars = 5719 × 19022
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
AnnData object with n_obs × n_vars = 5298 × 19113
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
AnnData object with n_obs × n_vars = 5601 × 18906
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
AnnData object with n_obs × n_vars = 6624 × 19110
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
AnnData object with n_obs × n_vars = 2692 × 14964
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'
AnnData object with n_obs × n_vars = 4119 × 17524
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'

First Sample: balsarc1 QC metrices cutoff: n_genes_by_counts,total_counts, pct_counts_mt and pct_counts_ribo

In [14]:
# Filter cells based on QC metrics
balsarc1 = balsarc1[balsarc1.obs.n_genes_by_counts < 4800, :]#The number of genes expressed in the count matrix
balsarc1 = balsarc1[balsarc1.obs.total_counts < 23000, :]#The total counts per cell
balsarc1 = balsarc1[balsarc1.obs.pct_counts_mt < 15, :] #The percentage of counts in mitochondrial genes
balsarc1 = balsarc1[balsarc1.obs.pct_counts_ribo <35, :] #The percentage of counts in ribosomal genes

Second Sample: balsarc2 QC metrices cutoff: n_genes_by_counts,total_counts, pct_counts_mt and pct_counts_ribo

In [15]:
# Filter cells based on QC metrics
balsarc2 = balsarc2[balsarc2.obs.n_genes_by_counts < 4000, :] #The number of genes expressed in the count matrix
balsarc2 = balsarc2[balsarc2.obs.total_counts < 17500, :] #The total counts per cell
balsarc2 = balsarc2[balsarc2.obs.pct_counts_mt < 20, :] #The percentage of counts in mitochondrial genes
balsarc2= balsarc2[balsarc2.obs.pct_counts_ribo <35, :]  #The percentage of counts in ribosomal genes

Third Sample: balsarc3 QC metrices cutoffs: n_genes_by_counts,total_counts, pct_counts_mt and pct_counts_ribo

In [16]:
# Filter cells based on QC metrics
balsarc3 = balsarc3[balsarc3.obs.n_genes_by_counts < 6500, :] #The number of genes expressed in the count matrix
balsarc3 = balsarc3[balsarc3.obs.total_counts < 40000, :] #The total counts per cell
balsarc3 = balsarc3[balsarc3.obs.pct_counts_mt < 18, :]  #The percentage of counts in mitochondrial genes
balsarc3 = balsarc3[balsarc3.obs.pct_counts_ribo <48, :]  #The percentage of counts in ribosomal genes

Fourth Sample:balhealthy1 QC metrices cutoffs: n_genes_by_counts,total_counts, pct_counts_mt and pct_counts_ribo

In [17]:
# Filter cells based on QC metrics
balhealthy1 = balhealthy1[balhealthy1.obs.n_genes_by_counts < 8000, :] #The number of genes expressed in the count matrix
balhealthy1 = balhealthy1[balhealthy1.obs.total_counts < 80000, :] #The total counts per cell
balhealthy1 = balhealthy1[balhealthy1.obs.pct_counts_mt < 20, :] #The percentage of counts in mitochondrial genes
balhealthy1 = balhealthy1[balhealthy1.obs.pct_counts_ribo <35, :] #The percentage of counts in ribosomal gene

Fifth Sample: balhealthy2 QC metrices cutoff: n_genes_by_counts,total_counts, pct_counts_mt and pct_counts_ribo

In [18]:
# Filter cells based on QC metrics
balhealthy2 = balhealthy2[balhealthy2.obs.n_genes_by_counts < 8000, :] #The number of genes expressed in the count matrix
balhealthy2 = balhealthy2[balhealthy2.obs.total_counts < 70000, :] #The total counts per cell
balhealthy2 = balhealthy2[balhealthy2.obs.pct_counts_mt < 19, :] #The percentage of counts in mitochondrial genes
balhealthy2 = balhealthy2[balhealthy2.obs.pct_counts_ribo <30, :]  #The percentage of counts in ribosomal genes

Sixth Sample: balhealthy3 QC metrices cutoffs: n_genes_by_counts,total_counts, pct_counts_mt and pct_counts_ribo

In [19]:
# Filter cells based on QC metrics
balhealthy3 = balhealthy3[balhealthy3.obs.n_genes_by_counts < 8200, :] #The number of genes expressed in the count matrix
balhealthy3 = balhealthy3[balhealthy3.obs.total_counts < 90000, :] #The total counts per cell
balhealthy3 = balhealthy3[balhealthy3.obs.pct_counts_mt < 20, :] #The percentage of counts in mitochondrial genes
balhealthy3 = balhealthy3[balhealthy3.obs.pct_counts_ribo <30, :] #The percentage of counts in ribosomal genes

Seventh Sample: balhealthy4 QC metrices cutoffs: n_genes_by_counts,total_counts, pct_counts_mt and pct_counts_ribo

In [20]:
# Filter cells based on QC metrics
balhealthy4 = balhealthy4[balhealthy4.obs.n_genes_by_counts < 7200, :] #The number of genes expressed in the count matrix
balhealthy4 = balhealthy4[balhealthy4.obs.total_counts < 70000, :] #The total counts per cell
balhealthy4 = balhealthy4[balhealthy4.obs.pct_counts_mt < 17.5, :] #The percentage of counts in mitochondrial genes
balhealthy4 = balhealthy4[balhealthy4.obs.pct_counts_ribo <40, :] #The percentage of counts in ribosomal genes

Eighth Sample: balhealthy5 QC metrices cutoffs: n_genes_by_counts,total_counts, pct_counts_mt and pct_counts_ribo

In [21]:
# Filter cells based on QC metrics
balhealthy5 = balhealthy5[balhealthy5.obs.n_genes_by_counts < 8000, :] #The number of genes expressed in the count matrix
balhealthy5 = balhealthy5[balhealthy5.obs.total_counts < 80000, :] #The total counts per cell
balhealthy5 = balhealthy5[balhealthy5.obs.pct_counts_mt < 17.5, :] #The percentage of counts in mitochondrial genes
balhealthy5 = balhealthy5[balhealthy5.obs.pct_counts_ribo <30, :] #The percentage of counts in ribosomal genes

Nineth Sample: balhealthy6 QC metrices cutoffs: n_genes_by_counts,total_counts, pct_counts_mt and pct_counts_ribo

In [22]:
# Filter cells based on QC metrics
balhealthy6 = balhealthy6[balhealthy6.obs.n_genes_by_counts < 8000, :] #The number of genes expressed in the count matrix
balhealthy6 = balhealthy6[balhealthy6.obs.total_counts < 100000, :] #The total counts per cell
balhealthy6 = balhealthy6[balhealthy6.obs.pct_counts_mt < 20, :] #The percentage of counts in mitochondrial genes
balhealthy6 = balhealthy6[balhealthy6.obs.pct_counts_ribo <45, :] #The percentage of counts in ribosomal genes

tenth Sample: balhealthy7 QC metrices cutoffs: n_genes_by_counts,total_counts, pct_counts_mt and pct_counts_ribo

In [23]:
# Filter cells based on QC metrics
balhealthy7 = balhealthy7[balhealthy7.obs.n_genes_by_counts < 8000, :] #The number of genes expressed in the count matrix
balhealthy7 = balhealthy7[balhealthy7.obs.total_counts < 70000, :] #The total counts per cell
balhealthy7 = balhealthy7[balhealthy7.obs.pct_counts_mt < 40, :] #The percentage of counts in mitochondrial genes
balhealthy7 = balhealthy7[balhealthy7.obs.pct_counts_ribo <35, :] #The percentage of counts in ribosomal genes

Eleventh Sample: balhealthy8 QC metrices cutoffs: n_genes_by_counts,total_counts, pct_counts_mt and pct_counts_ribo

In [24]:
# Filter cells based on QC metrics
balhealthy8 = balhealthy8[balhealthy8.obs.n_genes_by_counts < 8000, :] #The number of genes expressed in the count matrix
balhealthy8 = balhealthy8[balhealthy8.obs.total_counts < 80000, :] #The total counts per cell
balhealthy8 = balhealthy8[balhealthy8.obs.pct_counts_mt < 17.5, :] #The percentage of counts in mitochondrial genes
balhealthy8 = balhealthy8[balhealthy8.obs.pct_counts_ribo <35, :] #The percentage of counts in ribosomal genes

Tweleveth Sample: balhealthy9 QC metrices cutoffs: n_genes_by_counts,total_counts, pct_counts_mt and pct_counts_ribo

In [25]:
# Filter cells based on QC metrics
balhealthy9 = balhealthy9[balhealthy9.obs.n_genes_by_counts < 4500, :] #The number of genes expressed in the count matrix
balhealthy9 = balhealthy9[balhealthy9.obs.total_counts < 35000, :] #The total counts per cell
balhealthy9 = balhealthy9[balhealthy9.obs.pct_counts_mt < 15, :] #The percentage of counts in mitochondrial genes
balhealthy9 = balhealthy9[balhealthy9.obs.pct_counts_ribo <35, :] #The percentage of counts in ribosomal genes

Thirteenth Sample: balhealthy10 QC metrices cutoffs: n_genes_by_counts,total_counts, pct_counts_mt and pct_counts_ribo

In [26]:
# Filter cells based on QC metrics
balhealthy10 = balhealthy10[balhealthy10.obs.n_genes_by_counts < 7000, :] #The number of genes expressed in the count matrix
balhealthy10 = balhealthy10[balhealthy10.obs.total_counts < 70000, :] #The total counts per cell
balhealthy10 = balhealthy10[balhealthy10.obs.pct_counts_mt < 23, :] #The percentage of counts in mitochondrial genes
balhealthy10 = balhealthy10[balhealthy10.obs.pct_counts_ribo <30, :] #The percentage of counts in ribosomal genes

PostQC Violin plots sample wise

In [27]:
print("PostQC for First Sample: balsarc1")
sc.pl.violin(balsarc1, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)

print("PostQC for Second Sample: balsarc2")
sc.pl.violin(balsarc2, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)

print("PostQC for Third Sample: balsarc3")
sc.pl.violin(balsarc3, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)

print("PostQC for Fourth Sample: balhealthy1")
sc.pl.violin(balhealthy1, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)

print("PostQC for Fifth Sample: balhealthy2")
sc.pl.violin(balhealthy2, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)

print("PostQC for sixth Sample: balhealthy3")
sc.pl.violin(balhealthy3, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)

print("PostQC for seventh Sample: balhealthy4")
sc.pl.violin(balhealthy4, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)
print("PostQC for Eighth Sample: balhealthy5")
sc.pl.violin(balhealthy5, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)
print("PostQC for Nineth Sample: balhealthy6")
sc.pl.violin(balhealthy6, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)
print("PostQC for tenth Sample: balhealthy7")
sc.pl.violin(balhealthy7, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)
print("PostQC for Eleventh Sample: balhealthy8")
sc.pl.violin(balhealthy8, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)
print("PostQC for Tweleveth Sample: balhealthy9")
sc.pl.violin(balhealthy9, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)
print("PostQC for Thirteenth Sample: balhealthy10")
sc.pl.violin(balhealthy10, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt','pct_counts_ribo'],
                 jitter=0.4, multi_panel=True)

PostQC for First Sample: balsarc1

PostQC for Second Sample: balsarc2

PostQC for Third Sample: balsarc3

PostQC for Fourth Sample: balhealthy1

PostQC for Fifth Sample: balhealthy2

PostQC for sixth Sample: balhealthy3

PostQC for seventh Sample: balhealthy4

PostQC for Eighth Sample: balhealthy5

PostQC for Nineth Sample: balhealthy6

PostQC for tenth Sample: balhealthy7

PostQC for Eleventh Sample: balhealthy8

PostQC for Tweleveth Sample: balhealthy9

PostQC for Thirteenth Sample: balhealthy10

Normalization: Counts Per Million (CPM) ¶

In [28]:
#each cell by total counts over all genes,

# Assuming adata1 to adata7 are your datasets
adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]

# Normalization each cell by total counts over all genes (library-size correct) the data matrix to 10,000 reads per cell (target_sum=1e4)

for adata in adata_list:
    sc.pp.normalize_total(adata, target_sum=1e4)

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:170: UserWarning:

Received a view of an AnnData. Making a copy.


normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)

Logarithmize ¶

In [29]:
# Assuming adata1 to adata7 are your datasets
adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]

# Computes X=log(X+1) , where log denotes the natural logarithm
for adata in adata_list:
    sc.pp.log1p(adata)

Highly-variable genes filtering ¶

Identify highly-variable genes

In [30]:
# Assuming adata1 to adata7 are your datasets
adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]

# Apply log1p transformation to each adata
for adata in adata_list:
    sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)

extracting highly variable genes
    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
extracting highly variable genes
    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
extracting highly variable genes
    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
extracting highly variable genes
    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
extracting highly variable genes
    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
extracting highly variable genes
    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
extracting highly variable genes
    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
extracting highly variable genes
    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
extracting highly variable genes
    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
extracting highly variable genes
    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
extracting highly variable genes
    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)

Plotting highly-variable genes

In [31]:
# Assuming adata1 to adata7 are your datasets
adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]

# Total-count normalize (library-size correct) the data matrix to 10,000 reads per cell
for adata in adata_list:
    sc.pl.highly_variable_genes(adata)

Set to raw formats to all samples

In [32]:
adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]

# Set raw attribute for each adata
for adata in adata_list:
    adata.raw = adata

highly-variable genes filtering to all samples

In [33]:
adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]
# Set raw attribute for each adata
for adata in adata_list:
    adata = adata[:, adata.var.highly_variable]

Regressing out: total_counts', 'pct_counts_mt','pct_counts_ribo' to all samples ¶

The regression of total counts per cell, along with the percentage of mitochondrial genes and ribosomal genes, is a commonly employed technique that enhances the quality of scRNA-seq data analysis by mitigating confounding factors related to cell quality and technical variability. (PMID: 29752298)

In [34]:
#Regress out effects of total counts per cell and the percentage of mitochondrial genes expressed. 
adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]

# Set raw attribute for each adata
for adata in adata_list:
    sc.pp.regress_out(adata, ['total_counts', 'pct_counts_mt','pct_counts_ribo'])

regressing out ['total_counts', 'pct_counts_mt', 'pct_counts_ribo']
    sparse input is densified and may lead to high memory use
    finished (0:09:17)
regressing out ['total_counts', 'pct_counts_mt', 'pct_counts_ribo']
    sparse input is densified and may lead to high memory use
    finished (0:09:34)
regressing out ['total_counts', 'pct_counts_mt', 'pct_counts_ribo']
    sparse input is densified and may lead to high memory use
    finished (0:05:33)
regressing out ['total_counts', 'pct_counts_mt', 'pct_counts_ribo']
    sparse input is densified and may lead to high memory use
    finished (0:05:51)
regressing out ['total_counts', 'pct_counts_mt', 'pct_counts_ribo']
    sparse input is densified and may lead to high memory use
    finished (0:05:59)
regressing out ['total_counts', 'pct_counts_mt', 'pct_counts_ribo']
    sparse input is densified and may lead to high memory use
    finished (0:05:03)
regressing out ['total_counts', 'pct_counts_mt', 'pct_counts_ribo']
    sparse input is densified and may lead to high memory use
    finished (0:04:49)
regressing out ['total_counts', 'pct_counts_mt', 'pct_counts_ribo']
    sparse input is densified and may lead to high memory use
    finished (0:05:26)
regressing out ['total_counts', 'pct_counts_mt', 'pct_counts_ribo']
    sparse input is densified and may lead to high memory use
    finished (0:05:14)
regressing out ['total_counts', 'pct_counts_mt', 'pct_counts_ribo']
    sparse input is densified and may lead to high memory use
    finished (0:05:27)
regressing out ['total_counts', 'pct_counts_mt', 'pct_counts_ribo']
    sparse input is densified and may lead to high memory use
    finished (0:06:17)
regressing out ['total_counts', 'pct_counts_mt', 'pct_counts_ribo']
    sparse input is densified and may lead to high memory use
    finished (0:03:30)
regressing out ['total_counts', 'pct_counts_mt', 'pct_counts_ribo']
    sparse input is densified and may lead to high memory use
    finished (0:04:45)

Scaling ¶

In [35]:
# Scale each gene to unit variance up to standard deviation 10 to all samples
adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]


for adata in adata_list:
    sc.pp.scale(adata, max_value=10)

PCA Analysis ¶

In [36]:
import copy
import matplotlib.pyplot as plt

# Create a deep copy of adata_list
adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]
adata_list_copy = copy.deepcopy(adata_list)

# Initialize an empty list to store the variance ratios
variance_ratios = []

for n_pcs in range(1, 32):
    temp_variances = []
    for adata_temp in adata_list_copy:  # Iterate over each AnnData object
        adata_temp = adata_temp.copy()  # Create a copy of the AnnData object
        sc.tl.pca(adata_temp, n_comps=n_pcs, svd_solver='arpack')
        temp_variances.append(adata_temp.uns['pca']['variance_ratio'])
    variance_ratios.append(temp_variances)

# Plot the explained variance ratio for each PC
plt.figure(figsize=(10, 6))
for n_pcs in range(1, 32):
    for idx, var_ratio in enumerate(variance_ratios[n_pcs - 1], 1):
        plt.plot(range(1, n_pcs+1), var_ratio, marker='o', label=f'n_pcs={n_pcs}, dataset={idx}')
plt.xlabel('Number of PCs')
plt.ylabel('Explained Variance Ratio')
plt.title('Explained Variance Ratio for Each PC')
plt.legend(bbox_to_anchor=(1.4,0.8))
plt.show()

# Delete temporary objects
del adata_list_copy

computing PCA
    on highly variable genes
    with n_comps=1
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    on highly variable genes
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    on highly variable genes
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    on highly variable genes
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    on highly variable genes
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    on highly variable genes
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    on highly variable genes
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    on highly variable genes
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    on highly variable genes
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    on highly variable genes
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computing PCA
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    on highly variable genes
    with n_comps=28
    finished (0:00:02)
computing PCA
    on highly variable genes
    with n_comps=28
    finished (0:00:02)
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    on highly variable genes
    with n_comps=28
    finished (0:00:01)
computing PCA
    on highly variable genes
    with n_comps=28
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    on highly variable genes
    with n_comps=28
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    on highly variable genes
    with n_comps=28
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    on highly variable genes
    with n_comps=28
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    on highly variable genes
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    on highly variable genes
    with n_comps=28
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    on highly variable genes
    with n_comps=28
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computing PCA
    on highly variable genes
    with n_comps=29
    finished (0:00:04)
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    on highly variable genes
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    on highly variable genes
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    on highly variable genes
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    on highly variable genes
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    with n_comps=29
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    on highly variable genes
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    on highly variable genes
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    on highly variable genes
    with n_comps=30
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    on highly variable genes
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    on highly variable genes
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    on highly variable genes
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    on highly variable genes
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    on highly variable genes
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computing PCA
    on highly variable genes
    with n_comps=31
    finished (0:00:05)
computing PCA
    on highly variable genes
    with n_comps=31
    finished (0:00:03)
computing PCA
    on highly variable genes
    with n_comps=31
    finished (0:00:03)
computing PCA
    on highly variable genes
    with n_comps=31
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computing PCA
    on highly variable genes
    with n_comps=31
    finished (0:00:02)
computing PCA
    on highly variable genes
    with n_comps=31
    finished (0:00:03)
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    on highly variable genes
    with n_comps=31
    finished (0:00:04)
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    on highly variable genes
    with n_comps=31
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    on highly variable genes
    with n_comps=31
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    on highly variable genes
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    on highly variable genes
    with n_comps=31
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    on highly variable genes
    with n_comps=31
    finished (0:00:01)
computing PCA
    on highly variable genes
    with n_comps=31
    finished (0:00:01)

PCA analysis of first 20 PCs

In [38]:
adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]

# Set raw attribute for each adata
for adata in adata_list:
    sc.tl.pca(adata, svd_solver='arpack', n_comps=20)

computing PCA
    on highly variable genes
    with n_comps=20
    finished (0:00:02)
computing PCA
    on highly variable genes
    with n_comps=20
    finished (0:00:02)
computing PCA
    on highly variable genes
    with n_comps=20
    finished (0:00:02)
computing PCA
    on highly variable genes
    with n_comps=20
    finished (0:00:01)
computing PCA
    on highly variable genes
    with n_comps=20
    finished (0:00:01)
computing PCA
    on highly variable genes
    with n_comps=20
    finished (0:00:01)
computing PCA
    on highly variable genes
    with n_comps=20
    finished (0:00:02)
computing PCA
    on highly variable genes
    with n_comps=20
    finished (0:00:01)
computing PCA
    on highly variable genes
    with n_comps=20
    finished (0:00:01)
computing PCA
    on highly variable genes
    with n_comps=20
    finished (0:00:01)
computing PCA
    on highly variable genes
    with n_comps=20
    finished (0:00:02)
computing PCA
    on highly variable genes
    with n_comps=20
    finished (0:00:00)
computing PCA
    on highly variable genes
    with n_comps=20
    finished (0:00:01)

Scatter plot generation of some popular markers genes in the PCA coordinates

In [39]:
adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]

# Set raw attribute for each adata
for adata in adata_list:
    sc.pl.pca(adata, color= ['CD14', 'CD79A','CD3D', 'FCER1A','NKG7','CST3'])

#scatter plot generation in the PCA coordinates, with 'CD14', 'CD79A','CD3D', 'FCER1A','NKG7' and 'CST3'

print("CD14: CD14+ Monocytes, CD79A: B cell,  CD3D : CD4+ T cell, FCER1A: CD16+ Monocyte, NKG7: NK cell, CST3: Dendritic cells")

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.


/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.


/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.


/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.


/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.


/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.


/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.


/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.


/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.


/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.


/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.


/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.


/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.


CD14: CD14+ Monocytes, CD79A: B cell,  CD3D : CD4+ T cell, FCER1A: CD16+ Monocyte, NKG7: NK cell, CST3: Dendritic cells

Saving data ¶

In [40]:
import os
from scipy import io

save_files = [
    '/home/jana/balsarc1.h5ad',
    '/home/jana/balsarc2.h5ad',
    '/home/jana/balsarc3.h5ad',
    '/home/jana/balhealth1.h5ad',
    '/home/jana/balhealth2.h5ad',
    '/home/jana/balhealthy3.h5ad',
    '/home/jana/balhealthy4.h5ad',
    '/home/jana/balhealthy5.h5ad',
    '/home/jana/balhealthy6.h5ad',
    '/home/jana/balhealthy7.h5ad',
    '/home/jana/balhealthy8.h5ad',
    '/home/jana/balhealthy9.h5ad',
    '/home/jana/balhealthy10.h5ad'
]

adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]

# Save each adata to the corresponding file
for adata, save_file in zip(adata_list, save_files):
    adata.write_h5ad(save_file)

Deleting individual datasets to save space

In [41]:
# Deleting individual datasets to save space

del(balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10)

Reading dataset ¶

In [42]:
#Reading last saved annoatated data object written in h5ad data format. 
#We used similar adata variable to make similar previous data analysis 

# List of file paths
file_paths = [
    '/home/jana/balsarc1.h5ad',
    '/home/jana/balsarc2.h5ad',
    '/home/jana/balsarc3.h5ad',
    '/home/jana/balhealth1.h5ad',
    '/home/jana/balhealth2.h5ad',
    '/home/jana/balhealthy3.h5ad',
    '/home/jana/balhealthy4.h5ad',
    '/home/jana/balhealthy5.h5ad',
    '/home/jana/balhealthy6.h5ad',
    '/home/jana/balhealthy7.h5ad',
    '/home/jana/balhealthy8.h5ad',
    '/home/jana/balhealthy9.h5ad',
    '/home/jana/balhealthy10.h5ad'
]

# List to store loaded data objects
data_objects = []

# Loop to read h5ad files and store data objects
for file_path in file_paths:
    data_objects.append(sc.read_h5ad(file_path))

# Unpack data objects to individual variables
balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10 = data_objects

Displaying all samples in this workspace

In [44]:
adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]
for adata in adata_list:
    print (adata)

AnnData object with n_obs × n_vars = 11012 × 19931
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'feature_types', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
AnnData object with n_obs × n_vars = 11241 × 20264
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'feature_types', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
AnnData object with n_obs × n_vars = 4547 × 20484
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'feature_types', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
AnnData object with n_obs × n_vars = 5607 × 19466
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
AnnData object with n_obs × n_vars = 5710 × 19525
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
AnnData object with n_obs × n_vars = 4336 × 19223
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
AnnData object with n_obs × n_vars = 4397 × 18328
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
AnnData object with n_obs × n_vars = 5220 × 19022
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
AnnData object with n_obs × n_vars = 4731 × 19113
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
AnnData object with n_obs × n_vars = 5327 × 18906
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
AnnData object with n_obs × n_vars = 6108 × 19110
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
AnnData object with n_obs × n_vars = 2438 × 14964
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'
AnnData object with n_obs × n_vars = 3720 × 17524
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca'
    obsm: 'X_pca'
    varm: 'PCs'

Computing the neighborhood graph and clustering ¶

In [45]:
datasets = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]

# Compute UMAP coordinates for each dataset
for dataset in datasets:
    sc.pp.neighbors(dataset, n_neighbors=10, n_pcs=20)
    sc.tl.umap(dataset)
i=1
# Perform Leiden clustering for each dataset at different resolutions
for dataset in datasets:
    sc.tl.leiden(dataset)
    sc.tl.leiden(dataset, key_added="leiden_res0_20", resolution=0.20)
    sc.tl.leiden(dataset, key_added="leiden_res0_40", resolution=0.40)
    sc.tl.leiden(dataset, key_added="leiden_res0_60", resolution=0.60)
    sc.tl.leiden(dataset, key_added="leiden_res0_80", resolution=0.80)
    sc.tl.leiden(dataset, key_added="leiden_res1", resolution=1.0)

# Plot UMAP visualization with different cluster labels
    display ("sample no"+str(i))
    sc.pl.umap(dataset, color=["leiden_res0_20", "leiden_res0_40", "leiden_res0_60", "leiden_res0_80", "leiden_res1"], legend_loc="on data")
    i=i+1

computing neighbors
    using 'X_pca' with n_pcs = 20

/home/jana/my-notebook-venv/lib/python3.8/site-packages/numba/core/typed_passes.py:329: NumbaPerformanceWarning:


The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "my-notebook-venv/lib/python3.8/site-packages/umap/rp_tree.py", line 135:
@numba.njit(fastmath=True, nogil=True, parallel=True)
def euclidean_random_projection_split(data, indices, rng_state):
^


/home/jana/my-notebook-venv/lib/python3.8/site-packages/umap/nndescent.py:91: NumbaPerformanceWarning:


The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "my-notebook-venv/lib/python3.8/site-packages/umap/utils.py", line 409:
@numba.njit(parallel=True)
def build_candidates(current_graph, n_vertices, n_neighbors, max_candidates, rng_state):
^


/home/jana/my-notebook-venv/lib/python3.8/site-packages/numba/core/typed_passes.py:329: NumbaPerformanceWarning:


The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "my-notebook-venv/lib/python3.8/site-packages/umap/nndescent.py", line 47:
    @numba.njit(parallel=True)
    def nn_descent(
    ^



    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:14)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:17)
computing neighbors
    using 'X_pca' with n_pcs = 20

/home/jana/my-notebook-venv/lib/python3.8/site-packages/numba/core/typed_passes.py:329: NumbaPerformanceWarning:


The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "my-notebook-venv/lib/python3.8/site-packages/umap/nndescent.py", line 47:
    @numba.njit(parallel=True)
    def nn_descent(
    ^



    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:04)
computing UMAP

/home/jana/my-notebook-venv/lib/python3.8/site-packages/umap/spectral.py:227: UserWarning:

Embedding a total of 2 separate connected components using meta-embedding (experimental)


    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:13)
computing neighbors
    using 'X_pca' with n_pcs = 20
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:05)
computing UMAP

/home/jana/my-notebook-venv/lib/python3.8/site-packages/umap/spectral.py:227: UserWarning:

Embedding a total of 2 separate connected components using meta-embedding (experimental)


    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:14)
computing neighbors
    using 'X_pca' with n_pcs = 20
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:18)
computing neighbors
    using 'X_pca' with n_pcs = 20
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:18)
computing neighbors
    using 'X_pca' with n_pcs = 20
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:12)
computing neighbors
    using 'X_pca' with n_pcs = 20
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:12)
computing neighbors
    using 'X_pca' with n_pcs = 20
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:14)
computing neighbors
    using 'X_pca' with n_pcs = 20
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:13)
computing neighbors
    using 'X_pca' with n_pcs = 20
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:14)
computing neighbors
    using 'X_pca' with n_pcs = 20
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:17)
computing neighbors
    using 'X_pca' with n_pcs = 20
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:07)
computing neighbors
    using 'X_pca' with n_pcs = 20
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
computing UMAP

/home/jana/my-notebook-venv/lib/python3.8/site-packages/umap/spectral.py:227: UserWarning:

Embedding a total of 2 separate connected components using meta-embedding (experimental)


    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:11)
running Leiden clustering
    finished: found 17 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:02)
running Leiden clustering
    finished: found 5 clusters and added
    'leiden_res0_20', the cluster labels (adata.obs, categorical) (0:00:01)
running Leiden clustering
    finished: found 8 clusters and added
    'leiden_res0_40', the cluster labels (adata.obs, categorical) (0:00:01)
running Leiden clustering
    finished: found 10 clusters and added
    'leiden_res0_60', the cluster labels (adata.obs, categorical) (0:00:02)
running Leiden clustering
    finished: found 15 clusters and added
    'leiden_res0_80', the cluster labels (adata.obs, categorical) (0:00:02)
running Leiden clustering
    finished: found 17 clusters and added
    'leiden_res1', the cluster labels (adata.obs, categorical) (0:00:02)

'sample no1'
/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored


running Leiden clustering
    finished: found 20 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:01)
running Leiden clustering
    finished: found 9 clusters and added
    'leiden_res0_20', the cluster labels (adata.obs, categorical) (0:00:01)
running Leiden clustering
    finished: found 12 clusters and added
    'leiden_res0_40', the cluster labels (adata.obs, categorical) (0:00:01)
running Leiden clustering
    finished: found 16 clusters and added
    'leiden_res0_60', the cluster labels (adata.obs, categorical) (0:00:01)
running Leiden clustering
    finished: found 19 clusters and added
    'leiden_res0_80', the cluster labels (adata.obs, categorical) (0:00:01)
running Leiden clustering
    finished: found 20 clusters and added
    'leiden_res1', the cluster labels (adata.obs, categorical) (0:00:01)

'sample no2'
/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored


running Leiden clustering
    finished: found 21 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 9 clusters and added
    'leiden_res0_20', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 13 clusters and added
    'leiden_res0_40', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 16 clusters and added
    'leiden_res0_60', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 19 clusters and added
    'leiden_res0_80', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 21 clusters and added
    'leiden_res1', the cluster labels (adata.obs, categorical) (0:00:00)

'sample no3'
/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored


running Leiden clustering
    finished: found 19 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 7 clusters and added
    'leiden_res0_20', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 10 clusters and added
    'leiden_res0_40', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 12 clusters and added
    'leiden_res0_60', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 16 clusters and added
    'leiden_res0_80', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 19 clusters and added
    'leiden_res1', the cluster labels (adata.obs, categorical) (0:00:00)

'sample no4'
/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored


running Leiden clustering
    finished: found 17 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 7 clusters and added
    'leiden_res0_20', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 9 clusters and added
    'leiden_res0_40', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 11 clusters and added
    'leiden_res0_60', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 13 clusters and added
    'leiden_res0_80', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 17 clusters and added
    'leiden_res1', the cluster labels (adata.obs, categorical) (0:00:00)

'sample no5'
/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored


running Leiden clustering
    finished: found 21 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 6 clusters and added
    'leiden_res0_20', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 9 clusters and added
    'leiden_res0_40', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 12 clusters and added
    'leiden_res0_60', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 15 clusters and added
    'leiden_res0_80', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 21 clusters and added
    'leiden_res1', the cluster labels (adata.obs, categorical) (0:00:00)

'sample no6'
/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored


running Leiden clustering
    finished: found 19 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 5 clusters and added
    'leiden_res0_20', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 8 clusters and added
    'leiden_res0_40', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 11 clusters and added
    'leiden_res0_60', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 14 clusters and added
    'leiden_res0_80', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 19 clusters and added
    'leiden_res1', the cluster labels (adata.obs, categorical) (0:00:00)

'sample no7'
/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored


running Leiden clustering
    finished: found 19 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 5 clusters and added
    'leiden_res0_20', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 7 clusters and added
    'leiden_res0_40', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 12 clusters and added
    'leiden_res0_60', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 14 clusters and added
    'leiden_res0_80', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 19 clusters and added
    'leiden_res1', the cluster labels (adata.obs, categorical) (0:00:00)

'sample no8'
/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored


running Leiden clustering
    finished: found 19 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 7 clusters and added
    'leiden_res0_20', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 12 clusters and added
    'leiden_res0_40', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 14 clusters and added
    'leiden_res0_60', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 17 clusters and added
    'leiden_res0_80', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 19 clusters and added
    'leiden_res1', the cluster labels (adata.obs, categorical) (0:00:00)

'sample no9'
/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored


running Leiden clustering
    finished: found 20 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 6 clusters and added
    'leiden_res0_20', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 10 clusters and added
    'leiden_res0_40', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 12 clusters and added
    'leiden_res0_60', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 15 clusters and added
    'leiden_res0_80', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 20 clusters and added
    'leiden_res1', the cluster labels (adata.obs, categorical) (0:00:00)

'sample no10'
/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored


running Leiden clustering
    finished: found 17 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 5 clusters and added
    'leiden_res0_20', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 9 clusters and added
    'leiden_res0_40', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 11 clusters and added
    'leiden_res0_60', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 15 clusters and added
    'leiden_res0_80', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 17 clusters and added
    'leiden_res1', the cluster labels (adata.obs, categorical) (0:00:00)

'sample no11'
/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored


running Leiden clustering
    finished: found 13 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 4 clusters and added
    'leiden_res0_20', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 7 clusters and added
    'leiden_res0_40', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 8 clusters and added
    'leiden_res0_60', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 11 clusters and added
    'leiden_res0_80', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 13 clusters and added
    'leiden_res1', the cluster labels (adata.obs, categorical) (0:00:00)

'sample no12'
/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored


running Leiden clustering
    finished: found 19 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 6 clusters and added
    'leiden_res0_20', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 8 clusters and added
    'leiden_res0_40', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 13 clusters and added
    'leiden_res0_60', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 16 clusters and added
    'leiden_res0_80', the cluster labels (adata.obs, categorical) (0:00:00)
running Leiden clustering
    finished: found 19 clusters and added
    'leiden_res1', the cluster labels (adata.obs, categorical) (0:00:00)

'sample no13'
/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:163: MatplotlibDeprecationWarning:

The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning:

No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored

Displaying all samples in this workspace

In [46]:
adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]
for adata in adata_list:
    print (adata)

AnnData object with n_obs × n_vars = 11012 × 19931
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'leiden', 'leiden_res0_20', 'leiden_res0_40', 'leiden_res0_60', 'leiden_res0_80', 'leiden_res1'
    var: 'gene_ids', 'feature_types', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_res0_20_colors', 'leiden_res0_40_colors', 'leiden_res0_60_colors', 'leiden_res0_80_colors', 'leiden_res1_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'
AnnData object with n_obs × n_vars = 11241 × 20264
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'leiden', 'leiden_res0_20', 'leiden_res0_40', 'leiden_res0_60', 'leiden_res0_80', 'leiden_res1'
    var: 'gene_ids', 'feature_types', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_res0_20_colors', 'leiden_res0_40_colors', 'leiden_res0_60_colors', 'leiden_res0_80_colors', 'leiden_res1_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'
AnnData object with n_obs × n_vars = 4547 × 20484
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'leiden', 'leiden_res0_20', 'leiden_res0_40', 'leiden_res0_60', 'leiden_res0_80', 'leiden_res1'
    var: 'gene_ids', 'feature_types', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_res0_20_colors', 'leiden_res0_40_colors', 'leiden_res0_60_colors', 'leiden_res0_80_colors', 'leiden_res1_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'
AnnData object with n_obs × n_vars = 5607 × 19466
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'leiden', 'leiden_res0_20', 'leiden_res0_40', 'leiden_res0_60', 'leiden_res0_80', 'leiden_res1'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_res0_20_colors', 'leiden_res0_40_colors', 'leiden_res0_60_colors', 'leiden_res0_80_colors', 'leiden_res1_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'
AnnData object with n_obs × n_vars = 5710 × 19525
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'leiden', 'leiden_res0_20', 'leiden_res0_40', 'leiden_res0_60', 'leiden_res0_80', 'leiden_res1'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_res0_20_colors', 'leiden_res0_40_colors', 'leiden_res0_60_colors', 'leiden_res0_80_colors', 'leiden_res1_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'
AnnData object with n_obs × n_vars = 4336 × 19223
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'leiden', 'leiden_res0_20', 'leiden_res0_40', 'leiden_res0_60', 'leiden_res0_80', 'leiden_res1'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_res0_20_colors', 'leiden_res0_40_colors', 'leiden_res0_60_colors', 'leiden_res0_80_colors', 'leiden_res1_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'
AnnData object with n_obs × n_vars = 4397 × 18328
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'leiden', 'leiden_res0_20', 'leiden_res0_40', 'leiden_res0_60', 'leiden_res0_80', 'leiden_res1'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_res0_20_colors', 'leiden_res0_40_colors', 'leiden_res0_60_colors', 'leiden_res0_80_colors', 'leiden_res1_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'
AnnData object with n_obs × n_vars = 5220 × 19022
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'leiden', 'leiden_res0_20', 'leiden_res0_40', 'leiden_res0_60', 'leiden_res0_80', 'leiden_res1'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_res0_20_colors', 'leiden_res0_40_colors', 'leiden_res0_60_colors', 'leiden_res0_80_colors', 'leiden_res1_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'
AnnData object with n_obs × n_vars = 4731 × 19113
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'leiden', 'leiden_res0_20', 'leiden_res0_40', 'leiden_res0_60', 'leiden_res0_80', 'leiden_res1'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_res0_20_colors', 'leiden_res0_40_colors', 'leiden_res0_60_colors', 'leiden_res0_80_colors', 'leiden_res1_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'
AnnData object with n_obs × n_vars = 5327 × 18906
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'leiden', 'leiden_res0_20', 'leiden_res0_40', 'leiden_res0_60', 'leiden_res0_80', 'leiden_res1'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_res0_20_colors', 'leiden_res0_40_colors', 'leiden_res0_60_colors', 'leiden_res0_80_colors', 'leiden_res1_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'
AnnData object with n_obs × n_vars = 6108 × 19110
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'leiden', 'leiden_res0_20', 'leiden_res0_40', 'leiden_res0_60', 'leiden_res0_80', 'leiden_res1'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_res0_20_colors', 'leiden_res0_40_colors', 'leiden_res0_60_colors', 'leiden_res0_80_colors', 'leiden_res1_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'
AnnData object with n_obs × n_vars = 2438 × 14964
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'leiden', 'leiden_res0_20', 'leiden_res0_40', 'leiden_res0_60', 'leiden_res0_80', 'leiden_res1'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_res0_20_colors', 'leiden_res0_40_colors', 'leiden_res0_60_colors', 'leiden_res0_80_colors', 'leiden_res1_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'
AnnData object with n_obs × n_vars = 3720 × 17524
    obs: 'type', 'sample', 'percent_chrY', 'XIST-counts', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'leiden', 'leiden_res0_20', 'leiden_res0_40', 'leiden_res0_60', 'leiden_res0_80', 'leiden_res1'
    var: 'gene_ids', 'n_cells', 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'hvg', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_res0_20_colors', 'leiden_res0_40_colors', 'leiden_res0_60_colors', 'leiden_res0_80_colors', 'leiden_res1_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'

Visualize highest expression genes

In [47]:
# Visualize highest expression genes for each dataset in separate panels using a for loop
# Explore the loaded data for each dataset using a for loop


adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]

def highest_expr_genes(adata_list, n_top=20):
    for i, adata in enumerate(adata_list):
        sc.pl.highest_expr_genes(adata, n_top=n_top, show=False)
        plt.title(f'sample {i+1}')
        plt.show()
        

# Example usage:
highest_expr_genes(adata_list, n_top=20)

normalizing counts per cell

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:197: UserWarning:

Some cells have zero counts


    finished (0:00:00)

normalizing counts per cell

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:197: UserWarning:

Some cells have zero counts


    finished (0:00:00)

normalizing counts per cell

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:197: UserWarning:

Some cells have zero counts


    finished (0:00:00)

normalizing counts per cell

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:197: UserWarning:

Some cells have zero counts


    finished (0:00:00)

normalizing counts per cell

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:197: UserWarning:

Some cells have zero counts


    finished (0:00:00)

normalizing counts per cell

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:197: UserWarning:

Some cells have zero counts


    finished (0:00:00)

normalizing counts per cell

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:197: UserWarning:

Some cells have zero counts


    finished (0:00:00)

normalizing counts per cell

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:197: UserWarning:

Some cells have zero counts


    finished (0:00:00)

normalizing counts per cell

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:197: UserWarning:

Some cells have zero counts


    finished (0:00:00)

normalizing counts per cell

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:197: UserWarning:

Some cells have zero counts


    finished (0:00:00)

normalizing counts per cell

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:197: UserWarning:

Some cells have zero counts


    finished (0:00:00)

normalizing counts per cell
    finished (0:00:00)

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:197: UserWarning:

Some cells have zero counts


normalizing counts per cell
    finished (0:00:00)

/home/jana/my-notebook-venv/lib/python3.8/site-packages/scanpy/preprocessing/_normalization.py:197: UserWarning:

Some cells have zero counts

Saving data after neighborhood graph and clustering ¶

In [48]:
save_files = [
    '/home/jana/balsarc1.h5ad',
    '/home/jana/balsarc2.h5ad',
    '/home/jana/balsarc3.h5ad',
    '/home/jana/balhealth1.h5ad',
    '/home/jana/balhealth2.h5ad',
    '/home/jana/balhealthy3.h5ad',
    '/home/jana/balhealthy4.h5ad',
    '/home/jana/balhealthy5.h5ad',
    '/home/jana/balhealthy6.h5ad',
    '/home/jana/balhealthy7.h5ad',
    '/home/jana/balhealthy8.h5ad',
    '/home/jana/balhealthy9.h5ad',
    '/home/jana/balhealthy10.h5ad'
]

adata_list = [balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10]

# Save each adata to the corresponding file
for adata, save_file in zip(adata_list, save_files):
    adata.write_h5ad(save_file)

Deleting individual datasets to save space

In [49]:
# Deleting individual datasets to save space

del(balsarc1, balsarc2, balsarc3, balhealthy1, balhealthy2, balhealthy3, balhealthy4, balhealthy5, balhealthy6, balhealthy7, balhealthy8, balhealthy9, balhealthy10)
In [ ]: